Bulent Aydogan, PhD, Associate Professor, Department of Radiation and Cellular Oncology, University of Chicago, Chicago, IL, USA. James Radosevich, PhD, Professor, Department of Oral Medicine and Diagnostic Sciences, University of Illinois at Chicago, Chicago, IL, USA.

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• List of Contributors xiiiPreface xvList of Abbreviations xvii1 Genomic Strategies for Personalized Cancer TherapyArkadiusz Z. Dudek, Kate Baxstrom, Sushma Bharadwaj, Anne Blaes, Amit Kulkarni, Emil Lou, Vijeyaluxmy Nehru, Emma Rabinovich, Ardaman Shergill, and Maya Viner1.1 Introduction 11.1.1 Definition of Precision Medicine in Oncology 11.1.2 DNA and RNA Sequencing Techniques 21.2 Precision Medicine in Specific Tumors 31.2.1 Lung Cancer 31.2.1.1 Adenocarcinoma 41.2.1.2 Squamous Cell Carcinoma 41.2.1.3 Small-Cell Lung Carcinoma (SCLC) 41.2.1.4 Epidermal Growth Factor Receptor (EGFR) Mutations 41.2.1.5 Anaplastic Lymphoma Kinase (ALK) 51.2.1.6 BRAF, ROS1, MET 51.2.1.7 KRAS 61.2.1.8 Other: RET, NTRK 61.2.2 Head and Neck Cancers 61.2.2.1 HPV-Positive Cancers 71.2.2.2 HPV-Negative Cancers 81.2.2.3 Targeting the Epidermal Growth Factor Receptor (EGFR) Pathway 81.2.2.4 Thyroid Cancers 81.2.2.5 Other Targets 81.2.3 Hematological Malignancies 91.2.3.1 Lymphoma 91.2.3.2 Leukemia 101.2.3.3 Myelodysplastic Syndrome 111.2.4 Gynecologic Malignancies 111.2.4.1 Cervical 111.2.4.2 Uterine 111.2.4.3 Ovarian 121.2.5 Melanoma 131.2.6 Gastrointestinal Malignancies 161.2.6.1 Gastroesophageal Cancers 171.2.6.2 Colorectal Cancers 171.2.7 Breast Cancer 191.2.7.1 Basal-Like, or Triple Negative Breast Cancer 191.2.7.2 Luminal A/B, or Hormone Positive 201.2.7.3 HER2 Positive Breast Cancer 201.2.7.4 Immunotherapy 201.2.7.5 Germline Testing in Breast Cancer 211.2.7.6 Conclusion 211.2.8 Genitourinary Malignancies 211.2.8.1 Prostate Cancer 211.2.8.2 Renal Cell Cancer (RCC) 231.2.8.3 Urothelial Cancers 231.2.9 Pediatric Cancers 241.2.9.1 Introduction 241.2.9.2 Leukemia and Lymphoma 241.2.9.3 Central and Peripheral Nervous System Tumors 251.2.9.4 Bone and Soft Tissue Sarcomas 261.2.9.5 Other Embryonal Tumors 261.2.9.6 Conclusion 271.2.10 Cancers of Unknown Primary Origin 271.2.10.1 Diagnosis 271.2.10.2 Gene Expression Profiling 281.2.10.3 Mutational Testing with Next-Generation Sequencing (NGS) 281.2.10.4 Treatment 281.3 Biomarkers for Immunotherapy of Cancer 281.3.1 PD-L1 291.3.2 Soluble PD-L1 (sPD-L1) 291.3.3 Combined Positive Score (CPS) 301.3.4 Tumor Microenvironment 301.3.5 Tumor Mutational Burden (TMB) 301.3.6 Microsatellite Instability (MSI) 311.3.7 MMR Deficiency 311.3.8 Peripheral Blood Absolute Neutrophil Count/Absolute Lymphocyte Count 311.3.9 Microbiome 311.4 Clinical Trial Design in the Era of Precision Oncology 321.5 Ethical, Legal, and Social Issues of Precision Oncology 331.5.1 Ethical Issues 331.5.2 Legal Issues 341.5.3 Social Issues 351.6 Databases, Data Sharing, and Challenges of Precision Oncology 36References 372 Blood-Based Biomarkers for the Diagnosis and Prognosis of Cancer 61Shreetama Bandyopadhayaya and Chandi C. Mandal2.1 Introduction 612.2 Importance of Blood-Based Biomarkers 612.3 Circulating Proteins as Biomarkers 622.4 Circulating Long Non-coding RNAs as Biomarkers 642.5 Circulating miRNAs as Biomarkers 652.6 Circulating Autoantibodies as Biomarkers 672.7 Circulating Tumor DNA as Biomarkers 692.8 Metabolites as Biomarkers 702.9 Lipids as Biomarkers 722.10 Exosomes as Biomarkers 742.11 Conclusion 77References 773 Application of Circulating Cell-free DNA for Personalized Cancer Therapy 83Indranil Chattopadhyay3.1 Introduction 833.2 Drawbacks and Challenges of Invasive Tumor Tissue in Treatment and Diagnosis of Cancer 843.3 Importance of Noninvasive Biomarkers in Treatment and Diagnosis of Cancer 843.4 Liquid Biopsy: cfDNA and ctDNA 853.4.1 Biogenesis of ctDNA: Mechanisms of Release, Characteristics, Quantity, and Quality 853.4.2 Role of Preanalytical Factors that Affect cfDNA Measurements 863.5 Practical Approach to Estimate ctDNA in Liquid Biopsy 863.5.1 Isolation of cfDNA and ctDNA 863.5.2 Analysis of ctDNA by Real-Time Quantitative PCR 863.5.3 Analysis of ctDNA by Digital PCR (dPCR) 873.5.4 Analysis of ctDNA by Beads, Emulsion, Amplification, and Magnetics (BEAMing) 873.5.5 Analysis of ctDNA by Next-Generation Sequencing (NGS) 873.6 Clinical Application of ctDNA Detection in Various Cancers 883.6.1 Clinical Applications of ctDNA in Lung Cancer 883.6.2 Clinical Application of ctDNA in Head and Neck Cancer 893.6.3 Clinical Utility of Circulating Tumor DNA in Pancreatic Cancer 903.6.4 Clinical Utility of Circulating Tumor DNA in Early and Metastatic Breast Cancer 903.6.5 Clinical Utility of Circulating Tumor DNA in Colorectal Cancer 913.6.6 Clinical Utility of Circulating Tumor DNA in Melanoma 913.7 Clinical Utility of Methylation in ctDNA in Personalized Oncology 923.8 Conclusion 92References 934 Prognostic Implications of EGFR, p53, p16, Cyclin D1, and Bcl-2 in Head and Neck Squamous Cell Carcinoma (HNSCC) 99Zane Deliu, Ardaman Shergill, Anne Meier, Phyo Thazin Myint, Sarah Khan, Paramjeet Khosla, and Lawrence Feldman4.1 Introduction 994.2 Epidermal Growth Factor Receptor (EGFR) 994.2.1 EGFR Structure and Ligands 994.2.2 Physiology 1004.2.3 EGFR Expression and Genetic Changes 1004.2.3.1 EGFR Expression in HNSCC 1004.2.3.2 Normal Adjacent Oral Mucosa and Pre-malignant Lesions 1014.2.4 EGFR Genetic Changes: Gene Copy Numbers, Amplifications, and Mutations in HNSCC 1014.2.4.1 Association of EGFR Expression or Genetic Changes with HPV Infection 1014.2.5 EGFR as a Prognostic and Predictive Marker 1024.2.5.1 EGFR as a Prognostic Marker 1024.2.5.2 EGFR as a Predictive Marker 1024.2.6 Future Perspectives 1034.2.6.1 EGFR in Immuno-SPECT or PET Imaging 1034.2.6.2 Molecular Profiling for Precision Medicine 1044.3 TP53 Mutations in Head and Neck Cancer 1044.3.1 Pathogenesis and Prevalence 1044.3.2 Risk Factors 1044.3.3 TP53 Structure and Physiology 1054.3.3.1 TP53 Structure 1054.3.3.2 TP53 as a Tumor Suppressor Gene 1054.3.4 TP53 Gain of Function Properties 1054.3.5 TP53 as a Prognostic and Predictive Marker 1064.3.6 Therapeutic Strategies Targeting TP53 1064.4 P16 and Cyclin D1 Mutations in Head and Neck Cancer 1074.4.1 Cyclin D1 1074.4.2 P16 1084.5 Bcl-2 Mutations in Head and Neck Cancer 1094.5.1 Bcl-2 1094.5.1.1 Physiological Role of Bcl-2 1094.5.2 Bcl-2 Family of Proteins 1094.5.3 Significance of Bcl-2 Overexpression 1104.5.4 Association with Chemoresistance and Radioresistance 1114.5.5 Role of Bcl-2 as a Marker of Prognosis 1114.5.6 Chemotherapeutics Targeting Bcl-2 1124.5.7 Bcl-2 Summary 1134.6 Conclusion 113References 1145 Immunotherapy and Cancer 133Maaly Bassiony, Adedoyin Victoria Aluko, and James A. Radosevich5.1 Introduction 1335.2 What Is Cancer Immunotherapy? 1345.3 How Does Immunotherapy Work? 1355.4 Timing of Immunotherapy 1355.5 Combination Immunotherapy 1365.6 Side Effects of Immunotherapy 1375.7 Types of Cancer Immunotherapy Treatments 1375.7.1 Immune Checkpoint Inhibitors 1375.7.2 Monoclonal Antibodies and Tumor-Agnostic Therapies 1385.7.3 Adoptive T Cell Therapy 1385.8 Cancer Vaccines 1395.9 Oncolytic Viral Immunotherapy (OVIs) 1405.10 Non-specific Immunotherapies 1415.11 Immunotherapy by Cancer Type 1415.11.1 Skin Cancer 1415.11.2 Lung Cancer 1425.11.3 Breast Cancer 1425.11.4 Kidney and Prostate Cancers 1435.11.5 Brain Cancer 1455.11.6 Colorectal Cancer 1465.11.7 Bladder Cancer 1475.11.8 Cervical Cancer 1475.11.9 Leukemia 1475.11.10 Liver Cancer 1485.12 Proven Studies 1505.13 Cancer Immunity Pathway 1505.14 Recent Developments in Immunotherapy 1505.15 Neoantigens for Cancer Immunotherapy 1515.16 Discussion 152References 1536 Predictive and Prognostic Markers for Cancer Medicine 157Elif Zeynep Yilmaz and Ebru Esin Yoruker6.1 Introduction 1576.2 Historical Development of Cancer Markers 1576.3 Characteristics of the Ideal Cancer Markers 1586.3.1 Ideal Source of Cancer Markers 1596.3.2 Kinetics of Cancer Markers 1626.3.3 Sensitivity and Specificity for Evaluation of Cancer Markers 1636.4 Utilization of Cancer Markers in Most Common Cancers 1636.4.1 Colorectal Cancer 1666.4.1.1 CEA 1666.4.1.2 KRAS/NRAS 1676.4.1.3 MSI 1686.4.1.4 PD-1/PD – L1 1686.4.1.5 BRAF 1696.4.1.6 Oncotype DX Colon Cancer Test 1696.4.1.7 ColoPrint 1696.4.1.8 CTC 1696.4.2 Breast Cancer 1696.4.2.1 ER/PR 1706.4.2.2 HER2 1716.4.2.3 Oncotype DX 1716.4.2.4 MammaPrint 1716.4.2.5 uPA/PAI-1 1726.4.3 Ovarian Cancer 1726.4.4 Lung Cancer 1726.4.4.1 EGFR 1736.4.4.2 ALK Rearrangements 1736.4.4.3 ROS1 Rearragements 1746.4.5 Urological Cancers 1746.4.5.1 Prostate Cancer 1746.4.5.2 Renal Cancer 1756.5 Classification and Techniques for Studying of Cancer Markers 1766.5.1 Circulating Tumor Cells as Cancer Markers 1766.5.2 DNA-Based Cancer Markers 1776.5.2.1 Microsatellite Alterations 1776.5.2.2 cfDNA Integrıty 1776.5.2.3 DNA Methylation 1786.5.2.4 Mutations and Single Nucleotide Polymorphisms (SNPs) 1786.5.3 RNA-Based Tumor Markers 1806.5.3.1 mRNAs 1806.5.3.2 Noncoding RNAs 1806.5.4 Protein-Based Tumor Markers 1816.6 Clinical Validation of Cancer Markers 1856.7 Conclusions and Future Perspectives 186References 1877 Dual Energy Imaging in Precision Radiation Therapy 203John C. Roeske, Maksat Haytmyradov, Roberto Cassetta, and Murat Surucu7.1 Introduction and Overview 2037.2 Historical Perspective 2037.2.1 X-Ray Production 2047.2.2 X-Ray Interactions in Matter 2057.2.3 Planar Image Formation 2057.2.4 Computed Tomography 2067.2.5 X-Ray Imaging in Radiation Oncology 2077.2.6 Dual Energy Imaging in Radiation Therapy 2077.3 Planar Dual Energy Imaging 2087.3.1 Theory 2097.3.2 Planar Dual Energy Imaging Methods 2107.3.3 Applications in Radiation Therapy 2107.3.3.1 Image-Guided Radiation Therapy 2117.3.3.2 Markerless Tumor Tracking 2117.3.3.3 Megavoltage Dual Energy Imaging 2147.4 Dual Energy Computed Tomography 2147.4.1 Theory 2157.4.2 Dual Energy Scanning Methods 2167.4.3 Applications in Radiation Therapy 2187.4.3.1 Brachytherapy Planning 2187.4.3.2 Proton Planning 2187.4.3.3 Normal Tissue Segmentation 2207.4.3.4 Assessment of Therapy Response 2217.4.3.5 Dual Energy Cone Beam Computed Tomography 2227.5 Summary and Future Directions 222Acknowledgement 223References 2248 The Role of Big Data in Personalized Medicine 229Jean-Emmanuel Bibault and Lei Xing8.1 Introduction 2298.2 The Concept of Big Data and the Specificities of Healthcare 2308.2.1 Volume: How Big Is Big Data? 2308.2.2 Variety: Where Does Big Data Come from? 2318.2.3 Velocity: How Fast Is Big Data Generated and Interpreted? 2328.2.4 Variability: How Does Big Data Change? 2328.2.5 Veracity: How Accurate Is Big Data? 2328.2.6 Value: Why Is Big Data Important? 2328.3 Sources of Data 2338.3.1 Genomics, Epigenomics, and Transcriptomics 2338.3.2 Proteomics and Metabolomics 2348.3.3 Medical Imaging and Radiomics 2358.3.4 Clinical Informatics 2368.4 Big Data Analytical Techniques 2368.4.1 Machine Learning 2368.4.2 Deep Learning 2378.4.3 Natural Language Processing 2388.5 Challenges in Big Data Analytics 2398.5.1 Implementing a Big Data Approach 2398.5.2 Developing an Information-Sharing Culture 2398.5.3 Security Measures 2408.5.4 Ethics in Big Data Analysis 2408.5.4.1 Consent in the Era of Big Data 2408.5.4.2 Privacy 2408.5.4.3 Anonymization 2418.5.4.4 Ownership 241References 241Index 249

Bulent Aydogan, PhD, Associate Professor, Department of Radiation and Cellular Oncology, University of Chicago, Chicago, IL, USA. James Radosevich, PhD, Professor, Department of Oral Medicine and Diagnostic Sciences, University of Illinois at Chicago, Chicago, IL, USA.

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• List of Contributors xiiiPreface xvList of Abbreviations xvii1 Genomic Strategies for Personalized Cancer TherapyArkadiusz Z. Dudek, Kate Baxstrom, Sushma Bharadwaj, Anne Blaes, Amit Kulkarni, Emil Lou, Vijeyaluxmy Nehru, Emma Rabinovich, Ardaman Shergill, and Maya Viner1.1 Introduction 11.1.1 Definition of Precision Medicine in Oncology 11.1.2 DNA and RNA Sequencing Techniques 21.2 Precision Medicine in Specific Tumors 31.2.1 Lung Cancer 31.2.1.1 Adenocarcinoma 41.2.1.2 Squamous Cell Carcinoma 41.2.1.3 Small-Cell Lung Carcinoma (SCLC) 41.2.1.4 Epidermal Growth Factor Receptor (EGFR) Mutations 41.2.1.5 Anaplastic Lymphoma Kinase (ALK) 51.2.1.6 BRAF, ROS1, MET 51.2.1.7 KRAS 61.2.1.8 Other: RET, NTRK 61.2.2 Head and Neck Cancers 61.2.2.1 HPV-Positive Cancers 71.2.2.2 HPV-Negative Cancers 81.2.2.3 Targeting the Epidermal Growth Factor Receptor (EGFR) Pathway 81.2.2.4 Thyroid Cancers 81.2.2.5 Other Targets 81.2.3 Hematological Malignancies 91.2.3.1 Lymphoma 91.2.3.2 Leukemia 101.2.3.3 Myelodysplastic Syndrome 111.2.4 Gynecologic Malignancies 111.2.4.1 Cervical 111.2.4.2 Uterine 111.2.4.3 Ovarian 121.2.5 Melanoma 131.2.6 Gastrointestinal Malignancies 161.2.6.1 Gastroesophageal Cancers 171.2.6.2 Colorectal Cancers 171.2.7 Breast Cancer 191.2.7.1 Basal-Like, or Triple Negative Breast Cancer 191.2.7.2 Luminal A/B, or Hormone Positive 201.2.7.3 HER2 Positive Breast Cancer 201.2.7.4 Immunotherapy 201.2.7.5 Germline Testing in Breast Cancer 211.2.7.6 Conclusion 211.2.8 Genitourinary Malignancies 211.2.8.1 Prostate Cancer 211.2.8.2 Renal Cell Cancer (RCC) 231.2.8.3 Urothelial Cancers 231.2.9 Pediatric Cancers 241.2.9.1 Introduction 241.2.9.2 Leukemia and Lymphoma 241.2.9.3 Central and Peripheral Nervous System Tumors 251.2.9.4 Bone and Soft Tissue Sarcomas 261.2.9.5 Other Embryonal Tumors 261.2.9.6 Conclusion 271.2.10 Cancers of Unknown Primary Origin 271.2.10.1 Diagnosis 271.2.10.2 Gene Expression Profiling 281.2.10.3 Mutational Testing with Next-Generation Sequencing (NGS) 281.2.10.4 Treatment 281.3 Biomarkers for Immunotherapy of Cancer 281.3.1 PD-L1 291.3.2 Soluble PD-L1 (sPD-L1) 291.3.3 Combined Positive Score (CPS) 301.3.4 Tumor Microenvironment 301.3.5 Tumor Mutational Burden (TMB) 301.3.6 Microsatellite Instability (MSI) 311.3.7 MMR Deficiency 311.3.8 Peripheral Blood Absolute Neutrophil Count/Absolute Lymphocyte Count 311.3.9 Microbiome 311.4 Clinical Trial Design in the Era of Precision Oncology 321.5 Ethical, Legal, and Social Issues of Precision Oncology 331.5.1 Ethical Issues 331.5.2 Legal Issues 341.5.3 Social Issues 351.6 Databases, Data Sharing, and Challenges of Precision Oncology 36References 372 Blood-Based Biomarkers for the Diagnosis and Prognosis of Cancer 61Shreetama Bandyopadhayaya and Chandi C. Mandal2.1 Introduction 612.2 Importance of Blood-Based Biomarkers 612.3 Circulating Proteins as Biomarkers 622.4 Circulating Long Non-coding RNAs as Biomarkers 642.5 Circulating miRNAs as Biomarkers 652.6 Circulating Autoantibodies as Biomarkers 672.7 Circulating Tumor DNA as Biomarkers 692.8 Metabolites as Biomarkers 702.9 Lipids as Biomarkers 722.10 Exosomes as Biomarkers 742.11 Conclusion 77References 773 Application of Circulating Cell-free DNA for Personalized Cancer Therapy 83Indranil Chattopadhyay3.1 Introduction 833.2 Drawbacks and Challenges of Invasive Tumor Tissue in Treatment and Diagnosis of Cancer 843.3 Importance of Noninvasive Biomarkers in Treatment and Diagnosis of Cancer 843.4 Liquid Biopsy: cfDNA and ctDNA 853.4.1 Biogenesis of ctDNA: Mechanisms of Release, Characteristics, Quantity, and Quality 853.4.2 Role of Preanalytical Factors that Affect cfDNA Measurements 863.5 Practical Approach to Estimate ctDNA in Liquid Biopsy 863.5.1 Isolation of cfDNA and ctDNA 863.5.2 Analysis of ctDNA by Real-Time Quantitative PCR 863.5.3 Analysis of ctDNA by Digital PCR (dPCR) 873.5.4 Analysis of ctDNA by Beads, Emulsion, Amplification, and Magnetics (BEAMing) 873.5.5 Analysis of ctDNA by Next-Generation Sequencing (NGS) 873.6 Clinical Application of ctDNA Detection in Various Cancers 883.6.1 Clinical Applications of ctDNA in Lung Cancer 883.6.2 Clinical Application of ctDNA in Head and Neck Cancer 893.6.3 Clinical Utility of Circulating Tumor DNA in Pancreatic Cancer 903.6.4 Clinical Utility of Circulating Tumor DNA in Early and Metastatic Breast Cancer 903.6.5 Clinical Utility of Circulating Tumor DNA in Colorectal Cancer 913.6.6 Clinical Utility of Circulating Tumor DNA in Melanoma 913.7 Clinical Utility of Methylation in ctDNA in Personalized Oncology 923.8 Conclusion 92References 934 Prognostic Implications of EGFR, p53, p16, Cyclin D1, and Bcl-2 in Head and Neck Squamous Cell Carcinoma (HNSCC) 99Zane Deliu, Ardaman Shergill, Anne Meier, Phyo Thazin Myint, Sarah Khan, Paramjeet Khosla, and Lawrence Feldman4.1 Introduction 994.2 Epidermal Growth Factor Receptor (EGFR) 994.2.1 EGFR Structure and Ligands 994.2.2 Physiology 1004.2.3 EGFR Expression and Genetic Changes 1004.2.3.1 EGFR Expression in HNSCC 1004.2.3.2 Normal Adjacent Oral Mucosa and Pre-malignant Lesions 1014.2.4 EGFR Genetic Changes: Gene Copy Numbers, Amplifications, and Mutations in HNSCC 1014.2.4.1 Association of EGFR Expression or Genetic Changes with HPV Infection 1014.2.5 EGFR as a Prognostic and Predictive Marker 1024.2.5.1 EGFR as a Prognostic Marker 1024.2.5.2 EGFR as a Predictive Marker 1024.2.6 Future Perspectives 1034.2.6.1 EGFR in Immuno-SPECT or PET Imaging 1034.2.6.2 Molecular Profiling for Precision Medicine 1044.3 TP53 Mutations in Head and Neck Cancer 1044.3.1 Pathogenesis and Prevalence 1044.3.2 Risk Factors 1044.3.3 TP53 Structure and Physiology 1054.3.3.1 TP53 Structure 1054.3.3.2 TP53 as a Tumor Suppressor Gene 1054.3.4 TP53 Gain of Function Properties 1054.3.5 TP53 as a Prognostic and Predictive Marker 1064.3.6 Therapeutic Strategies Targeting TP53 1064.4 P16 and Cyclin D1 Mutations in Head and Neck Cancer 1074.4.1 Cyclin D1 1074.4.2 P16 1084.5 Bcl-2 Mutations in Head and Neck Cancer 1094.5.1 Bcl-2 1094.5.1.1 Physiological Role of Bcl-2 1094.5.2 Bcl-2 Family of Proteins 1094.5.3 Significance of Bcl-2 Overexpression 1104.5.4 Association with Chemoresistance and Radioresistance 1114.5.5 Role of Bcl-2 as a Marker of Prognosis 1114.5.6 Chemotherapeutics Targeting Bcl-2 1124.5.7 Bcl-2 Summary 1134.6 Conclusion 113References 1145 Immunotherapy and Cancer 133Maaly Bassiony, Adedoyin Victoria Aluko, and James A. Radosevich5.1 Introduction 1335.2 What Is Cancer Immunotherapy? 1345.3 How Does Immunotherapy Work? 1355.4 Timing of Immunotherapy 1355.5 Combination Immunotherapy 1365.6 Side Effects of Immunotherapy 1375.7 Types of Cancer Immunotherapy Treatments 1375.7.1 Immune Checkpoint Inhibitors 1375.7.2 Monoclonal Antibodies and Tumor-Agnostic Therapies 1385.7.3 Adoptive T Cell Therapy 1385.8 Cancer Vaccines 1395.9 Oncolytic Viral Immunotherapy (OVIs) 1405.10 Non-specific Immunotherapies 1415.11 Immunotherapy by Cancer Type 1415.11.1 Skin Cancer 1415.11.2 Lung Cancer 1425.11.3 Breast Cancer 1425.11.4 Kidney and Prostate Cancers 1435.11.5 Brain Cancer 1455.11.6 Colorectal Cancer 1465.11.7 Bladder Cancer 1475.11.8 Cervical Cancer 1475.11.9 Leukemia 1475.11.10 Liver Cancer 1485.12 Proven Studies 1505.13 Cancer Immunity Pathway 1505.14 Recent Developments in Immunotherapy 1505.15 Neoantigens for Cancer Immunotherapy 1515.16 Discussion 152References 1536 Predictive and Prognostic Markers for Cancer Medicine 157Elif Zeynep Yilmaz and Ebru Esin Yoruker6.1 Introduction 1576.2 Historical Development of Cancer Markers 1576.3 Characteristics of the Ideal Cancer Markers 1586.3.1 Ideal Source of Cancer Markers 1596.3.2 Kinetics of Cancer Markers 1626.3.3 Sensitivity and Specificity for Evaluation of Cancer Markers 1636.4 Utilization of Cancer Markers in Most Common Cancers 1636.4.1 Colorectal Cancer 1666.4.1.1 CEA 1666.4.1.2 KRAS/NRAS 1676.4.1.3 MSI 1686.4.1.4 PD-1/PD – L1 1686.4.1.5 BRAF 1696.4.1.6 Oncotype DX Colon Cancer Test 1696.4.1.7 ColoPrint 1696.4.1.8 CTC 1696.4.2 Breast Cancer 1696.4.2.1 ER/PR 1706.4.2.2 HER2 1716.4.2.3 Oncotype DX 1716.4.2.4 MammaPrint 1716.4.2.5 uPA/PAI-1 1726.4.3 Ovarian Cancer 1726.4.4 Lung Cancer 1726.4.4.1 EGFR 1736.4.4.2 ALK Rearrangements 1736.4.4.3 ROS1 Rearragements 1746.4.5 Urological Cancers 1746.4.5.1 Prostate Cancer 1746.4.5.2 Renal Cancer 1756.5 Classification and Techniques for Studying of Cancer Markers 1766.5.1 Circulating Tumor Cells as Cancer Markers 1766.5.2 DNA-Based Cancer Markers 1776.5.2.1 Microsatellite Alterations 1776.5.2.2 cfDNA Integrıty 1776.5.2.3 DNA Methylation 1786.5.2.4 Mutations and Single Nucleotide Polymorphisms (SNPs) 1786.5.3 RNA-Based Tumor Markers 1806.5.3.1 mRNAs 1806.5.3.2 Noncoding RNAs 1806.5.4 Protein-Based Tumor Markers 1816.6 Clinical Validation of Cancer Markers 1856.7 Conclusions and Future Perspectives 186References 1877 Dual Energy Imaging in Precision Radiation Therapy 203John C. Roeske, Maksat Haytmyradov, Roberto Cassetta, and Murat Surucu7.1 Introduction and Overview 2037.2 Historical Perspective 2037.2.1 X-Ray Production 2047.2.2 X-Ray Interactions in Matter 2057.2.3 Planar Image Formation 2057.2.4 Computed Tomography 2067.2.5 X-Ray Imaging in Radiation Oncology 2077.2.6 Dual Energy Imaging in Radiation Therapy 2077.3 Planar Dual Energy Imaging 2087.3.1 Theory 2097.3.2 Planar Dual Energy Imaging Methods 2107.3.3 Applications in Radiation Therapy 2107.3.3.1 Image-Guided Radiation Therapy 2117.3.3.2 Markerless Tumor Tracking 2117.3.3.3 Megavoltage Dual Energy Imaging 2147.4 Dual Energy Computed Tomography 2147.4.1 Theory 2157.4.2 Dual Energy Scanning Methods 2167.4.3 Applications in Radiation Therapy 2187.4.3.1 Brachytherapy Planning 2187.4.3.2 Proton Planning 2187.4.3.3 Normal Tissue Segmentation 2207.4.3.4 Assessment of Therapy Response 2217.4.3.5 Dual Energy Cone Beam Computed Tomography 2227.5 Summary and Future Directions 222Acknowledgement 223References 2248 The Role of Big Data in Personalized Medicine 229Jean-Emmanuel Bibault and Lei Xing8.1 Introduction 2298.2 The Concept of Big Data and the Specificities of Healthcare 2308.2.1 Volume: How Big Is Big Data? 2308.2.2 Variety: Where Does Big Data Come from? 2318.2.3 Velocity: How Fast Is Big Data Generated and Interpreted? 2328.2.4 Variability: How Does Big Data Change? 2328.2.5 Veracity: How Accurate Is Big Data? 2328.2.6 Value: Why Is Big Data Important? 2328.3 Sources of Data 2338.3.1 Genomics, Epigenomics, and Transcriptomics 2338.3.2 Proteomics and Metabolomics 2348.3.3 Medical Imaging and Radiomics 2358.3.4 Clinical Informatics 2368.4 Big Data Analytical Techniques 2368.4.1 Machine Learning 2368.4.2 Deep Learning 2378.4.3 Natural Language Processing 2388.5 Challenges in Big Data Analytics 2398.5.1 Implementing a Big Data Approach 2398.5.2 Developing an Information-Sharing Culture 2398.5.3 Security Measures 2408.5.4 Ethics in Big Data Analysis 2408.5.4.1 Consent in the Era of Big Data 2408.5.4.2 Privacy 2408.5.4.3 Anonymization 2418.5.4.4 Ownership 241References 241Index 249

Bulent Aydogan, PhD, Associate Professor, Department of Radiation and Cellular Oncology, University of Chicago, Chicago, IL, USA. James Radosevich, PhD, Professor, Department of Oral Medicine and Diagnostic Sciences, University of Illinois at Chicago, Chicago, IL, USA.

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• List of Contributors xiiiPreface xvList of Abbreviations xvii1 Genomic Strategies for Personalized Cancer TherapyArkadiusz Z. Dudek, Kate Baxstrom, Sushma Bharadwaj, Anne Blaes, Amit Kulkarni, Emil Lou, Vijeyaluxmy Nehru, Emma Rabinovich, Ardaman Shergill, and Maya Viner1.1 Introduction 11.1.1 Definition of Precision Medicine in Oncology 11.1.2 DNA and RNA Sequencing Techniques 21.2 Precision Medicine in Specific Tumors 31.2.1 Lung Cancer 31.2.1.1 Adenocarcinoma 41.2.1.2 Squamous Cell Carcinoma 41.2.1.3 Small-Cell Lung Carcinoma (SCLC) 41.2.1.4 Epidermal Growth Factor Receptor (EGFR) Mutations 41.2.1.5 Anaplastic Lymphoma Kinase (ALK) 51.2.1.6 BRAF, ROS1, MET 51.2.1.7 KRAS 61.2.1.8 Other: RET, NTRK 61.2.2 Head and Neck Cancers 61.2.2.1 HPV-Positive Cancers 71.2.2.2 HPV-Negative Cancers 81.2.2.3 Targeting the Epidermal Growth Factor Receptor (EGFR) Pathway 81.2.2.4 Thyroid Cancers 81.2.2.5 Other Targets 81.2.3 Hematological Malignancies 91.2.3.1 Lymphoma 91.2.3.2 Leukemia 101.2.3.3 Myelodysplastic Syndrome 111.2.4 Gynecologic Malignancies 111.2.4.1 Cervical 111.2.4.2 Uterine 111.2.4.3 Ovarian 121.2.5 Melanoma 131.2.6 Gastrointestinal Malignancies 161.2.6.1 Gastroesophageal Cancers 171.2.6.2 Colorectal Cancers 171.2.7 Breast Cancer 191.2.7.1 Basal-Like, or Triple Negative Breast Cancer 191.2.7.2 Luminal A/B, or Hormone Positive 201.2.7.3 HER2 Positive Breast Cancer 201.2.7.4 Immunotherapy 201.2.7.5 Germline Testing in Breast Cancer 211.2.7.6 Conclusion 211.2.8 Genitourinary Malignancies 211.2.8.1 Prostate Cancer 211.2.8.2 Renal Cell Cancer (RCC) 231.2.8.3 Urothelial Cancers 231.2.9 Pediatric Cancers 241.2.9.1 Introduction 241.2.9.2 Leukemia and Lymphoma 241.2.9.3 Central and Peripheral Nervous System Tumors 251.2.9.4 Bone and Soft Tissue Sarcomas 261.2.9.5 Other Embryonal Tumors 261.2.9.6 Conclusion 271.2.10 Cancers of Unknown Primary Origin 271.2.10.1 Diagnosis 271.2.10.2 Gene Expression Profiling 281.2.10.3 Mutational Testing with Next-Generation Sequencing (NGS) 281.2.10.4 Treatment 281.3 Biomarkers for Immunotherapy of Cancer 281.3.1 PD-L1 291.3.2 Soluble PD-L1 (sPD-L1) 291.3.3 Combined Positive Score (CPS) 301.3.4 Tumor Microenvironment 301.3.5 Tumor Mutational Burden (TMB) 301.3.6 Microsatellite Instability (MSI) 311.3.7 MMR Deficiency 311.3.8 Peripheral Blood Absolute Neutrophil Count/Absolute Lymphocyte Count 311.3.9 Microbiome 311.4 Clinical Trial Design in the Era of Precision Oncology 321.5 Ethical, Legal, and Social Issues of Precision Oncology 331.5.1 Ethical Issues 331.5.2 Legal Issues 341.5.3 Social Issues 351.6 Databases, Data Sharing, and Challenges of Precision Oncology 36References 372 Blood-Based Biomarkers for the Diagnosis and Prognosis of Cancer 61Shreetama Bandyopadhayaya and Chandi C. Mandal2.1 Introduction 612.2 Importance of Blood-Based Biomarkers 612.3 Circulating Proteins as Biomarkers 622.4 Circulating Long Non-coding RNAs as Biomarkers 642.5 Circulating miRNAs as Biomarkers 652.6 Circulating Autoantibodies as Biomarkers 672.7 Circulating Tumor DNA as Biomarkers 692.8 Metabolites as Biomarkers 702.9 Lipids as Biomarkers 722.10 Exosomes as Biomarkers 742.11 Conclusion 77References 773 Application of Circulating Cell-free DNA for Personalized Cancer Therapy 83Indranil Chattopadhyay3.1 Introduction 833.2 Drawbacks and Challenges of Invasive Tumor Tissue in Treatment and Diagnosis of Cancer 843.3 Importance of Noninvasive Biomarkers in Treatment and Diagnosis of Cancer 843.4 Liquid Biopsy: cfDNA and ctDNA 853.4.1 Biogenesis of ctDNA: Mechanisms of Release, Characteristics, Quantity, and Quality 853.4.2 Role of Preanalytical Factors that Affect cfDNA Measurements 863.5 Practical Approach to Estimate ctDNA in Liquid Biopsy 863.5.1 Isolation of cfDNA and ctDNA 863.5.2 Analysis of ctDNA by Real-Time Quantitative PCR 863.5.3 Analysis of ctDNA by Digital PCR (dPCR) 873.5.4 Analysis of ctDNA by Beads, Emulsion, Amplification, and Magnetics (BEAMing) 873.5.5 Analysis of ctDNA by Next-Generation Sequencing (NGS) 873.6 Clinical Application of ctDNA Detection in Various Cancers 883.6.1 Clinical Applications of ctDNA in Lung Cancer 883.6.2 Clinical Application of ctDNA in Head and Neck Cancer 893.6.3 Clinical Utility of Circulating Tumor DNA in Pancreatic Cancer 903.6.4 Clinical Utility of Circulating Tumor DNA in Early and Metastatic Breast Cancer 903.6.5 Clinical Utility of Circulating Tumor DNA in Colorectal Cancer 913.6.6 Clinical Utility of Circulating Tumor DNA in Melanoma 913.7 Clinical Utility of Methylation in ctDNA in Personalized Oncology 923.8 Conclusion 92References 934 Prognostic Implications of EGFR, p53, p16, Cyclin D1, and Bcl-2 in Head and Neck Squamous Cell Carcinoma (HNSCC) 99Zane Deliu, Ardaman Shergill, Anne Meier, Phyo Thazin Myint, Sarah Khan, Paramjeet Khosla, and Lawrence Feldman4.1 Introduction 994.2 Epidermal Growth Factor Receptor (EGFR) 994.2.1 EGFR Structure and Ligands 994.2.2 Physiology 1004.2.3 EGFR Expression and Genetic Changes 1004.2.3.1 EGFR Expression in HNSCC 1004.2.3.2 Normal Adjacent Oral Mucosa and Pre-malignant Lesions 1014.2.4 EGFR Genetic Changes: Gene Copy Numbers, Amplifications, and Mutations in HNSCC 1014.2.4.1 Association of EGFR Expression or Genetic Changes with HPV Infection 1014.2.5 EGFR as a Prognostic and Predictive Marker 1024.2.5.1 EGFR as a Prognostic Marker 1024.2.5.2 EGFR as a Predictive Marker 1024.2.6 Future Perspectives 1034.2.6.1 EGFR in Immuno-SPECT or PET Imaging 1034.2.6.2 Molecular Profiling for Precision Medicine 1044.3 TP53 Mutations in Head and Neck Cancer 1044.3.1 Pathogenesis and Prevalence 1044.3.2 Risk Factors 1044.3.3 TP53 Structure and Physiology 1054.3.3.1 TP53 Structure 1054.3.3.2 TP53 as a Tumor Suppressor Gene 1054.3.4 TP53 Gain of Function Properties 1054.3.5 TP53 as a Prognostic and Predictive Marker 1064.3.6 Therapeutic Strategies Targeting TP53 1064.4 P16 and Cyclin D1 Mutations in Head and Neck Cancer 1074.4.1 Cyclin D1 1074.4.2 P16 1084.5 Bcl-2 Mutations in Head and Neck Cancer 1094.5.1 Bcl-2 1094.5.1.1 Physiological Role of Bcl-2 1094.5.2 Bcl-2 Family of Proteins 1094.5.3 Significance of Bcl-2 Overexpression 1104.5.4 Association with Chemoresistance and Radioresistance 1114.5.5 Role of Bcl-2 as a Marker of Prognosis 1114.5.6 Chemotherapeutics Targeting Bcl-2 1124.5.7 Bcl-2 Summary 1134.6 Conclusion 113References 1145 Immunotherapy and Cancer 133Maaly Bassiony, Adedoyin Victoria Aluko, and James A. Radosevich5.1 Introduction 1335.2 What Is Cancer Immunotherapy? 1345.3 How Does Immunotherapy Work? 1355.4 Timing of Immunotherapy 1355.5 Combination Immunotherapy 1365.6 Side Effects of Immunotherapy 1375.7 Types of Cancer Immunotherapy Treatments 1375.7.1 Immune Checkpoint Inhibitors 1375.7.2 Monoclonal Antibodies and Tumor-Agnostic Therapies 1385.7.3 Adoptive T Cell Therapy 1385.8 Cancer Vaccines 1395.9 Oncolytic Viral Immunotherapy (OVIs) 1405.10 Non-specific Immunotherapies 1415.11 Immunotherapy by Cancer Type 1415.11.1 Skin Cancer 1415.11.2 Lung Cancer 1425.11.3 Breast Cancer 1425.11.4 Kidney and Prostate Cancers 1435.11.5 Brain Cancer 1455.11.6 Colorectal Cancer 1465.11.7 Bladder Cancer 1475.11.8 Cervical Cancer 1475.11.9 Leukemia 1475.11.10 Liver Cancer 1485.12 Proven Studies 1505.13 Cancer Immunity Pathway 1505.14 Recent Developments in Immunotherapy 1505.15 Neoantigens for Cancer Immunotherapy 1515.16 Discussion 152References 1536 Predictive and Prognostic Markers for Cancer Medicine 157Elif Zeynep Yilmaz and Ebru Esin Yoruker6.1 Introduction 1576.2 Historical Development of Cancer Markers 1576.3 Characteristics of the Ideal Cancer Markers 1586.3.1 Ideal Source of Cancer Markers 1596.3.2 Kinetics of Cancer Markers 1626.3.3 Sensitivity and Specificity for Evaluation of Cancer Markers 1636.4 Utilization of Cancer Markers in Most Common Cancers 1636.4.1 Colorectal Cancer 1666.4.1.1 CEA 1666.4.1.2 KRAS/NRAS 1676.4.1.3 MSI 1686.4.1.4 PD-1/PD – L1 1686.4.1.5 BRAF 1696.4.1.6 Oncotype DX Colon Cancer Test 1696.4.1.7 ColoPrint 1696.4.1.8 CTC 1696.4.2 Breast Cancer 1696.4.2.1 ER/PR 1706.4.2.2 HER2 1716.4.2.3 Oncotype DX 1716.4.2.4 MammaPrint 1716.4.2.5 uPA/PAI-1 1726.4.3 Ovarian Cancer 1726.4.4 Lung Cancer 1726.4.4.1 EGFR 1736.4.4.2 ALK Rearrangements 1736.4.4.3 ROS1 Rearragements 1746.4.5 Urological Cancers 1746.4.5.1 Prostate Cancer 1746.4.5.2 Renal Cancer 1756.5 Classification and Techniques for Studying of Cancer Markers 1766.5.1 Circulating Tumor Cells as Cancer Markers 1766.5.2 DNA-Based Cancer Markers 1776.5.2.1 Microsatellite Alterations 1776.5.2.2 cfDNA Integrıty 1776.5.2.3 DNA Methylation 1786.5.2.4 Mutations and Single Nucleotide Polymorphisms (SNPs) 1786.5.3 RNA-Based Tumor Markers 1806.5.3.1 mRNAs 1806.5.3.2 Noncoding RNAs 1806.5.4 Protein-Based Tumor Markers 1816.6 Clinical Validation of Cancer Markers 1856.7 Conclusions and Future Perspectives 186References 1877 Dual Energy Imaging in Precision Radiation Therapy 203John C. Roeske, Maksat Haytmyradov, Roberto Cassetta, and Murat Surucu7.1 Introduction and Overview 2037.2 Historical Perspective 2037.2.1 X-Ray Production 2047.2.2 X-Ray Interactions in Matter 2057.2.3 Planar Image Formation 2057.2.4 Computed Tomography 2067.2.5 X-Ray Imaging in Radiation Oncology 2077.2.6 Dual Energy Imaging in Radiation Therapy 2077.3 Planar Dual Energy Imaging 2087.3.1 Theory 2097.3.2 Planar Dual Energy Imaging Methods 2107.3.3 Applications in Radiation Therapy 2107.3.3.1 Image-Guided Radiation Therapy 2117.3.3.2 Markerless Tumor Tracking 2117.3.3.3 Megavoltage Dual Energy Imaging 2147.4 Dual Energy Computed Tomography 2147.4.1 Theory 2157.4.2 Dual Energy Scanning Methods 2167.4.3 Applications in Radiation Therapy 2187.4.3.1 Brachytherapy Planning 2187.4.3.2 Proton Planning 2187.4.3.3 Normal Tissue Segmentation 2207.4.3.4 Assessment of Therapy Response 2217.4.3.5 Dual Energy Cone Beam Computed Tomography 2227.5 Summary and Future Directions 222Acknowledgement 223References 2248 The Role of Big Data in Personalized Medicine 229Jean-Emmanuel Bibault and Lei Xing8.1 Introduction 2298.2 The Concept of Big Data and the Specificities of Healthcare 2308.2.1 Volume: How Big Is Big Data? 2308.2.2 Variety: Where Does Big Data Come from? 2318.2.3 Velocity: How Fast Is Big Data Generated and Interpreted? 2328.2.4 Variability: How Does Big Data Change? 2328.2.5 Veracity: How Accurate Is Big Data? 2328.2.6 Value: Why Is Big Data Important? 2328.3 Sources of Data 2338.3.1 Genomics, Epigenomics, and Transcriptomics 2338.3.2 Proteomics and Metabolomics 2348.3.3 Medical Imaging and Radiomics 2358.3.4 Clinical Informatics 2368.4 Big Data Analytical Techniques 2368.4.1 Machine Learning 2368.4.2 Deep Learning 2378.4.3 Natural Language Processing 2388.5 Challenges in Big Data Analytics 2398.5.1 Implementing a Big Data Approach 2398.5.2 Developing an Information-Sharing Culture 2398.5.3 Security Measures 2408.5.4 Ethics in Big Data Analysis 2408.5.4.1 Consent in the Era of Big Data 2408.5.4.2 Privacy 2408.5.4.3 Anonymization 2418.5.4.4 Ownership 241References 241Index 249